Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015.

BACKGROUND: Exposure to ambient air pollution increases morbidity and mortality, and is a leading contributor to global disease burden. We explored spatial and temporal trends in mortality and burden of disease attributable to ambient air pollution from 1990 to 2015 at global, regional, and country levels. METHODS: We estimated global population-weighted mean concentrations of particle mass with aerodynamic diameter less than 2·5 µm (PM2·5) and ozone at an approximate 11 km × 11 km resolution with satellite-based estimates, chemical transport models, and ground-level measurements. Using integrated exposure-response functions for each cause of death, we estimated the relative risk of mortality from ischaemic heart disease, cerebrovascular disease, chronic obstructive pulmonary disease, lung cancer, and lower respiratory infections from epidemiological studies using non-linear exposure-response functions spanning the global range of exposure. FINDINGS: Ambient PM2·5 was the fifth-ranking mortality risk factor in 2015. Exposure to PM2·5 caused 4·2 million (95% uncertainty interval [UI] 3·7 million to 4·8 million) deaths and 103·1 million (90·8 million 115·1 million) disability-adjusted life-years (DALYs) in 2015, representing 7·6% of total global deaths and 4·2% of global DALYs, 59% of these in east and south Asia. Deaths attributable to ambient PM2·5 increased from 3·5 million (95% UI 3·0 million to 4·0 million) in 1990 to 4·2 million (3·7 million to 4·8 million) in 2015. Exposure to ozone caused an additional 254 000 (95% UI 97 000-422 000) deaths and a loss of 4·1 million (1·6 million to 6·8 million) DALYs from chronic obstructive pulmonary disease in 2015. INTERPRETATION: Ambient air pollution contributed substantially to the global burden of disease in 2015, which increased over the past 25 years, due to population ageing, changes in non-communicable disease rates, and increasing air pollution in low-income and middle-income countries. Modest reductions in burden will occur in the most polluted countries unless PM2·5 values are decreased substantially, but there is potential for substantial health benefits from exposure reduction. FUNDING: Bill & Melinda Gates Foundation and Health Effects Institute.

Long-term Concentrations of Nitrogen Dioxide and Mortality: A Meta-analysis of Cohort Studies.

BACKGROUND: Concentrations of outdoor nitrogen dioxide (NO2) have been associated with increased mortality. Hazard ratios (HRs) from cohort studies are used to assess population health impact and burden. We undertook meta-analyses to derive concentration-response functions suitable for such evaluations and assessed their sensitivity to study selection based upon cohort characteristics. METHODS: We searched online databases and existing reviews for cohort studies published to October 2016 that reported HRs for NO2 and mortality. We calculated meta-analytic summary estimates using fixed/random-effects models. RESULTS: We identified 48 articles analyzing 28 cohorts. Meta-analysis of HRs found positive associations between NO2 and all cause (1.02 [95% confidence interval (CI): 1.01, 1.03]; prediction interval [PI]: [0.99, 1.06] per 10 µg/m increment in NO2), cardiovascular (1.03 [95% CI: 1.02, 1.05]; PI: [0.98, 1.08]), respiratory (1.03 [95% CI: 1.01, 1.05]; PI: [0.97, 1.10]), and lung cancer mortality (1.05 [95% CI: 1.02, 1.08]; PI: [0.94, 1.17]) with evidence of substantial heterogeneity between studies. In subgroup analysis, summary HRs varied by age at cohort entry, spatial resolution of pollution estimates, and adjustment for smoking and body mass index at the individual level; for some subgroups, the HR was close to unity, with lower confidence limits below 1. CONCLUSIONS: Given the many uncertainties inherent in the assessment of this evidence base and the sensitivity of health impact calculations to small changes in the magnitude of the HRs, calculation of the impact on health of policies to reduce long-term exposure to NO2 should use prediction intervals and report ranges of impact rather than focusing upon point estimates.

Changes in health in England, with analysis by English regions and areas of deprivation, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013.

BACKGROUND: In the Global Burden of Disease Study 2013 (GBD 2013), knowledge about health and its determinants has been integrated into a comparable framework to inform health policy. Outputs of this analysis are relevant to current policy questions in England and elsewhere, particularly on health inequalities. We use GBD 2013 data on mortality and causes of death, and disease and injury incidence and prevalence to analyse the burden of disease and injury in England as a whole, in English regions, and within each English region by deprivation quintile. We also assess disease and injury burden in England attributable to potentially preventable risk factors. England and the English regions are compared with the remaining constituent countries of the UK and with comparable countries in the European Union (EU) and beyond. METHODS: We extracted data from the GBD 2013 to compare mortality, causes of death, years of life lost (YLLs), years lived with a disability (YLDs), and disability-adjusted life-years (DALYs) in England, the UK, and 18 other countries (the first 15 EU members [apart from the UK] and Australia, Canada, Norway, and the USA [EU15+]). We extended elements of the analysis to English regions, and subregional areas defined by deprivation quintile (deprivation areas). We used data split by the nine English regions (corresponding to the European boundaries of the Nomenclature for Territorial Statistics level 1 [NUTS 1] regions), and by quintile groups within each English region according to deprivation, thereby making 45 regional deprivation areas. Deprivation quintiles were defined by area of residence ranked at national level by Index of Multiple Deprivation score, 2010. Burden due to various risk factors is described for England using new GBD methodology to estimate independent and overlapping attributable risk for five tiers of behavioural, metabolic, and environmental risk factors. We present results for 306 causes and 2337 sequelae, and 79 risks or risk clusters. FINDINGS: Between 1990 and 2013, life expectancy from birth in England increased by 5·4 years (95% uncertainty interval 5·0-5·8) from 75·9 years (75·9-76·0) to 81·3 years (80·9-81·7); gains were greater for men than for women. Rates of age-standardised YLLs reduced by 41·1% (38·3-43·6), whereas DALYs were reduced by 23·8% (20·9-27·1), and YLDs by 1·4% (0·1-2·8). For these measures, England ranked better than the UK and the EU15+ means. Between 1990 and 2013, the range in life expectancy among 45 regional deprivation areas remained 8·2 years for men and decreased from 7·2 years in 1990 to 6·9 years in 2013 for women. In 2013, the leading cause of YLLs was ischaemic heart disease, and the leading cause of DALYs was low back and neck pain. Known risk factors accounted for 39·6% (37·7-41·7) of DALYs; leading behavioural risk factors were suboptimal diet (10·8% [9·1-12·7]) and tobacco (10·7% [9·4-12·0]). INTERPRETATION: Health in England is improving although substantial opportunities exist for further reductions in the burden of preventable disease. The gap in mortality rates between men and women has reduced, but marked health inequalities between the least deprived and most deprived areas remain. Declines in mortality have not been matched by similar declines in morbidity, resulting in people living longer with diseases. Health policies must therefore address the causes of ill health as well as those of premature mortality. Systematic action locally and nationally is needed to reduce risk exposures, support healthy behaviours, alleviate the severity of chronic disabling disorders, and mitigate the effects of socioeconomic deprivation. FUNDING: Bill & Melinda Gates Foundation and Public Health England.

Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013.

BACKGROUND: The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) is the first of a series of annual updates of the GBD. Risk factor quantification, particularly of modifiable risk factors, can help to identify emerging threats to population health and opportunities for prevention. The GBD 2013 provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution. METHODS: Attributable deaths, years of life lost, years lived with disability, and disability-adjusted life-years (DALYs) have been estimated for 79 risks or clusters of risks using the GBD 2010 methods. Risk-outcome pairs meeting explicit evidence criteria were assessed for 188 countries for the period 1990-2013 by age and sex using three inputs: risk exposure, relative risks, and the theoretical minimum risk exposure level (TMREL). Risks are organised into a hierarchy with blocks of behavioural, environmental and occupational, and metabolic risks at the first level of the hierarchy. The next level in the hierarchy includes nine clusters of related risks and two individual risks, with more detail provided at levels 3 and 4 of the hierarchy. Compared with GBD 2010, six new risk factors have been added: handwashing practices, occupational exposure to trichloroethylene, childhood wasting, childhood stunting, unsafe sex, and low glomerular filtration rate. For most risks, data for exposure were synthesised with a Bayesian meta-regression method, DisMod-MR 2.0, or spatial-temporal Gaussian process regression. Relative risks were based on meta-regressions of published cohort and intervention studies. Attributable burden for clusters of risks and all risks combined took into account evidence on the mediation of some risks such as high body-mass index (BMI) through other risks such as high systolic blood pressure and high cholesterol. FINDINGS: All risks combined account for 57·2% (95% uncertainty interval [UI] 55·8-58·5) of deaths and 41·6% (40·1-43·0) of DALYs. Risks quantified account for 87·9% (86·5-89·3) of cardiovascular disease DALYs, ranging to a low of 0% for neonatal disorders and neglected tropical diseases and malaria. In terms of global DALYs in 2013, six risks or clusters of risks each caused more than 5% of DALYs: dietary risks accounting for 11·3 million deaths and 241·4 million DALYs, high systolic blood pressure for 10·4 million deaths and 208·1 million DALYs, child and maternal malnutrition for 1·7 million deaths and 176·9 million DALYs, tobacco smoke for 6·1 million deaths and 143·5 million DALYs, air pollution for 5·5 million deaths and 141·5 million DALYs, and high BMI for 4·4 million deaths and 134·0 million DALYs. Risk factor patterns vary across regions and countries and with time. In sub-Saharan Africa, the leading risk factors are child and maternal malnutrition, unsafe sex, and unsafe water, sanitation, and handwashing. In women, in nearly all countries in the Americas, north Africa, and the Middle East, and in many other high-income countries, high BMI is the leading risk factor, with high systolic blood pressure as the leading risk in most of Central and Eastern Europe and south and east Asia. For men, high systolic blood pressure or tobacco use are the leading risks in nearly all high-income countries, in north Africa and the Middle East, Europe, and Asia. For men and women, unsafe sex is the leading risk in a corridor from Kenya to South Africa. INTERPRETATION: Behavioural, environmental and occupational, and metabolic risks can explain half of global mortality and more than one-third of global DALYs providing many opportunities for prevention. Of the larger risks, the attributable burden of high BMI has increased in the past 23 years. In view of the prominence of behavioural risk factors, behavioural and social science research on interventions for these risks should be strengthened. Many prevention and primary care policy options are available now to act on key risks. FUNDING: Bill & Melinda Gates Foundation.

Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990-2013: quantifying the epidemiological transition.

BACKGROUND: The Global Burden of Disease Study 2013 (GBD 2013) aims to bring together all available epidemiological data using a coherent measurement framework, standardised estimation methods, and transparent data sources to enable comparisons of health loss over time and across causes, age-sex groups, and countries. The GBD can be used to generate summary measures such as disability-adjusted life-years (DALYs) and healthy life expectancy (HALE) that make possible comparative assessments of broad epidemiological patterns across countries and time. These summary measures can also be used to quantify the component of variation in epidemiology that is related to sociodemographic development. METHODS: We used the published GBD 2013 data for age-specific mortality, years of life lost due to premature mortality (YLLs), and years lived with disability (YLDs) to calculate DALYs and HALE for 1990, 1995, 2000, 2005, 2010, and 2013 for 188 countries. We calculated HALE using the Sullivan method; 95% uncertainty intervals (UIs) represent uncertainty in age-specific death rates and YLDs per person for each country, age, sex, and year. We estimated DALYs for 306 causes for each country as the sum of YLLs and YLDs; 95% UIs represent uncertainty in YLL and YLD rates. We quantified patterns of the epidemiological transition with a composite indicator of sociodemographic status, which we constructed from income per person, average years of schooling after age 15 years, and the total fertility rate and mean age of the population. We applied hierarchical regression to DALY rates by cause across countries to decompose variance related to the sociodemographic status variable, country, and time. FINDINGS: Worldwide, from 1990 to 2013, life expectancy at birth rose by 6·2 years (95% UI 5·6-6·6), from 65·3 years (65·0-65·6) in 1990 to 71·5 years (71·0-71·9) in 2013, HALE at birth rose by 5·4 years (4·9-5·8), from 56·9 years (54·5-59·1) to 62·3 years (59·7-64·8), total DALYs fell by 3·6% (0·3-7·4), and age-standardised DALY rates per 100 000 people fell by 26·7% (24·6-29·1). For communicable, maternal, neonatal, and nutritional disorders, global DALY numbers, crude rates, and age-standardised rates have all declined between 1990 and 2013, whereas for non-communicable diseases, global DALYs have been increasing, DALY rates have remained nearly constant, and age-standardised DALY rates declined during the same period. From 2005 to 2013, the number of DALYs increased for most specific non-communicable diseases, including cardiovascular diseases and neoplasms, in addition to dengue, food-borne trematodes, and leishmaniasis; DALYs decreased for nearly all other causes. By 2013, the five leading causes of DALYs were ischaemic heart disease, lower respiratory infections, cerebrovascular disease, low back and neck pain, and road injuries. Sociodemographic status explained more than 50% of the variance between countries and over time for diarrhoea, lower respiratory infections, and other common infectious diseases; maternal disorders; neonatal disorders; nutritional deficiencies; other communicable, maternal, neonatal, and nutritional diseases; musculoskeletal disorders; and other non-communicable diseases. However, sociodemographic status explained less than 10% of the variance in DALY rates for cardiovascular diseases; chronic respiratory diseases; cirrhosis; diabetes, urogenital, blood, and endocrine diseases; unintentional injuries; and self-harm and interpersonal violence. Predictably, increased sociodemographic status was associated with a shift in burden from YLLs to YLDs, driven by declines in YLLs and increases in YLDs from musculoskeletal disorders, neurological disorders, and mental and substance use disorders. In most country-specific estimates, the increase in life expectancy was greater than that in HALE. Leading causes of DALYs are highly variable across countries. INTERPRETATION: Global health is improving. Population growth and ageing have driven up numbers of DALYs, but crude rates have remained relatively constant, showing that progress in health does not mean fewer demands on health systems. The notion of an epidemiological transition--in which increasing sociodemographic status brings structured change in disease burden--is useful, but there is tremendous variation in burden of disease that is not associated with sociodemographic status. This further underscores the need for country-specific assessments of DALYs and HALE to appropriately inform health policy decisions and attendant actions. FUNDING: Bill & Melinda Gates Foundation.

Ambient Air Pollution Exposure Estimation for the Global Burden of Disease 2013.

Exposure to ambient air pollution is a major risk factor for global disease. Assessment of the impacts of air pollution on population health and evaluation of trends relative to other major risk factors requires regularly updated, accurate, spatially resolved exposure estimates. We combined satellite-based estimates, chemical transport model simulations, and ground measurements from 79 different countries to produce global estimates of annual average fine particle (PM2.5) and ozone concentrations at 0.1° × 0.1° spatial resolution for five-year intervals from 1990 to 2010 and the year 2013. These estimates were applied to assess population-weighted mean concentrations for 1990-2013 for each of 188 countries. In 2013, 87% of the world's population lived in areas exceeding the World Health Organization Air Quality Guideline of 10 µg/m(3) PM2.5 (annual average). Between 1990 and 2013, global population-weighted PM2.5 increased by 20.4% driven by trends in South Asia, Southeast Asia, and China. Decreases in population-weighted mean concentrations of PM2.5 were evident in most high income countries. Population-weighted mean concentrations of ozone increased globally by 8.9% from 1990-2013 with increases in most countries-except for modest decreases in North America, parts of Europe, and several countries in Southeast Asia.

Associations of short-term exposure to traffic-related air pollution with cardiovascular and respiratory hospital admissions in London, UK.

OBJECTIVES: There is evidence of adverse associations between short-term exposure to traffic-related pollution and health, but little is known about the relative contribution of the various sources and particulate constituents. METHODS: For each day for 2011-2012 in London, UK over 100 air pollutant metrics were assembled using monitors, modelling and chemical analyses. We selected a priori metrics indicative of traffic sources: general traffic, petrol exhaust, diesel exhaust and non-exhaust (mineral dust, brake and tyre wear). Using Poisson regression models, controlling for time-varying confounders, we derived effect estimates for cardiovascular and respiratory hospital admissions at prespecified lags and evaluated the sensitivity of estimates to multipollutant modelling and effect modification by season. RESULTS: For single day exposure, we found consistent associations between adult (15-64 years) cardiovascular and paediatric (0-14 years) respiratory admissions with elemental and black carbon (EC/BC), ranging from 0.56% to 1.65% increase per IQR change, and to a lesser degree with carbon monoxide (CO) and aluminium (Al). The average of past 7 days EC/BC exposure was associated with elderly (65+ years) cardiovascular admissions. Indicated associations were higher during the warm period of the year. Although effect estimates were sensitive to the adjustment for other pollutants they remained consistent in direction, indicating independence of associations from different sources, especially between diesel and petrol engines, as well as mineral dust. CONCLUSIONS: Our results suggest that exhaust related pollutants are associated with increased numbers of adult cardiovascular and paediatric respiratory hospitalisations. More extensive monitoring in urban centres is required to further elucidate the associations.

UK health performance: findings of the Global Burden of Disease Study 2010.

BACKGROUND: The UK has had universal free health care and public health programmes for more than six decades. Several policy initiatives and structural reforms of the health system have been undertaken. Health expenditure has increased substantially since 1990, albeit from relatively low levels compared with other countries. We used data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2010 (GBD 2010) to examine the patterns of health loss in the UK, the leading preventable risks that explain some of these patterns, and how UK outcomes compare with a set of comparable countries in the European Union and elsewhere in 1990 and 2010. METHODS: We used results of GBD 2010 for 1990 and 2010 for the UK and 18 other comparator nations (the original 15 members of the European Union, Australia, Canada, Norway, and the USA; henceforth EU15+). We present analyses of trends and relative performance for mortality, causes of death, years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE). We present results for 259 diseases and injuries and for 67 risk factors or clusters of risk factors relevant to the UK. We assessed the UK's rank for age-standardised YLLs and DALYs for their leading causes compared with EU15+ in 1990 and 2010. We estimated 95% uncertainty intervals (UIs) for all measures. FINDINGS: For both mortality and disability, overall health has improved substantially in absolute terms in the UK from 1990 to 2010. Life expectancy in the UK increased by 4·2 years (95% UI 4·2-4·3) from 1990 to 2010. However, the UK performed significantly worse than the EU15+ for age-standardised death rates, age-standardised YLL rates, and life expectancy in 1990, and its relative position had worsened by 2010. Although in most age groups, there have been reductions in age-specific mortality, for men aged 30-34 years, mortality rates have hardly changed (reduction of 3·7%, 95% UI 2·7-4·9). In terms of premature mortality, worsening ranks are most notable for men and women aged 20-54 years. For all age groups, the contributions of Alzheimer's disease (increase of 137%, 16-277), cirrhosis (65%, ?15 to 107), and drug use disorders (577%, 71-942) to premature mortality rose from 1990 to 2010. In 2010, compared with EU15+, the UK had significantly lower rates of age-standardised YLLs for road injury, diabetes, liver cancer, and chronic kidney disease, but significantly greater rates for ischaemic heart disease, chronic obstructive pulmonary disease, lower respiratory infections, breast cancer, other cardiovascular and circulatory disorders, oesophageal cancer, preterm birth complications, congenital anomalies, and aortic aneurysm. Because YLDs per person by age and sex have not changed substantially from 1990 to 2010 but age-specific mortality has been falling, the importance of chronic disability is rising. The major causes of YLDs in 2010 were mental and behavioural disorders (including substance abuse; 21·5% [95 UI 17·2-26·3] of YLDs), and musculoskeletal disorders (30·5% [25·5-35·7]). The leading risk factor in the UK was tobacco (11·8% [10·5-13·3] of DALYs), followed by increased blood pressure (9·0 % [7·5-10·5]), and high body-mass index (8·6% [7·4-9·8]). Diet and physical inactivity accounted for 14·3% (95% UI 12·8-15·9) of UK DALYs in 2010. INTERPRETATION: The performance of the UK in terms of premature mortality is persistently and significantly below the mean of EU15+ and requires additional concerted action. Further progress in premature mortality from several major causes, such as cardiovascular diseases and cancers, will probably require improved public health, prevention, early intervention, and treatment activities. The growing burden of disability, particularly from mental disorders, substance use, musculoskeletal disorders, and falls deserves an integrated and strategic response. FUNDING: Bill & Melinda Gates Foundation.

Ambient air pollution: a cause of COPD?

The role of ambient air pollution in the development of chronic obstructive pulmonary disease (COPD) is considered to be uncertain. We review the evidence in the light of recent studies. Eight morbidity and six mortality studies were identified. These were heterogeneous in design, characterisation of exposure to air pollution and methods of outcome definition. Six morbidity studies with objectively defined COPD (forced expiratory volume in 1 s/forced vital capacity ratio) were cross-sectional analyses. One longitudinal study defined incidence of COPD as the first hospitalisation due to COPD. However, neither mortality nor hospitalisation studies can unambiguously distinguish acute from long-term effects on the development of the underlying pathophysiological changes. Most studies were based on within-community exposure contrasts, which mainly assess traffic-related air pollution. Overall, evidence of chronic effects of air pollution on the prevalence and incidence of COPD among adults was suggestive but not conclusive, despite plausible biological mechanisms and good evidence that air pollution affects lung development in childhood and triggers exacerbations in COPD patients. To fully integrate this evidence in the assessment, the life-time course of COPD should be better defined. Larger studies with longer follow-up periods, specific definitions of COPD phenotypes, and more refined and source-specific exposure assessments are needed.

Mortality associations with long-term exposure to outdoor air pollution in a national English cohort.

RATIONALE: Cohort evidence linking long-term exposure to outdoor particulate air pollution and mortality has come largely from the United States. There is relatively little evidence from nationally representative cohorts in other countries. OBJECTIVES: To investigate the relationship between long-term exposure to a range of pollutants and causes of death in a national English cohort. METHODS: A total of 835,607 patients aged 40-89 years registered with 205 general practices were followed from 2003-2007. Annual average concentrations in 2002 for particulate matter with a median aerodynamic diameter less than 10 (PM(10)) and less than 2.5 µm (PM(2.5)), nitrogen dioxide (NO(2)), ozone, and sulfur dioxide (SO(2)) at 1 km(2) resolution, estimated from emission-based models, were linked to residential postcode. Deaths (n = 83,103) were ascertained from linkage to death certificates, and hazard ratios (HRs) for all- and cause-specific mortality for pollutants were estimated for interquartile pollutant changes from Cox models adjusting for age, sex, smoking, body mass index, and area-level socioeconomic status markers. MEASUREMENTS AND MAIN RESULTS: Residential concentrations of all pollutants except ozone were positively associated with all-cause mortality (HR, 1.02, 1.03, and 1.04 for PM(2.5), NO(2), and SO(2), respectively). Associations for PM(2.5), NO(2), and SO(2) were larger for respiratory deaths (HR, 1.09 each) and lung cancer (HR, 1.02, 1.06, and 1.05) but nearer unity for cardiovascular deaths (1.00, 1.00, and 1.04). CONCLUSIONS: These results strengthen the evidence linking long-term ambient air pollution exposure to increased all-cause mortality. However, the stronger associations with respiratory mortality are not consistent with most US studies in which associations with cardiovascular causes of death tend to predominate.

Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010.

BACKGROUND: Reliable and timely information on the leading causes of death in populations, and how these are changing, is a crucial input into health policy debates. In the Global Burden of Diseases, Injuries, and Risk Factors Study 2010 (GBD 2010), we aimed to estimate annual deaths for the world and 21 regions between 1980 and 2010 for 235 causes, with uncertainty intervals (UIs), separately by age and sex. METHODS: We attempted to identify all available data on causes of death for 187 countries from 1980 to 2010 from vital registration, verbal autopsy, mortality surveillance, censuses, surveys, hospitals, police records, and mortuaries. We assessed data quality for completeness, diagnostic accuracy, missing data, stochastic variations, and probable causes of death. We applied six different modelling strategies to estimate cause-specific mortality trends depending on the strength of the data. For 133 causes and three special aggregates we used the Cause of Death Ensemble model (CODEm) approach, which uses four families of statistical models testing a large set of different models using different permutations of covariates. Model ensembles were developed from these component models. We assessed model performance with rigorous out-of-sample testing of prediction error and the validity of 95% UIs. For 13 causes with low observed numbers of deaths, we developed negative binomial models with plausible covariates. For 27 causes for which death is rare, we modelled the higher level cause in the cause hierarchy of the GBD 2010 and then allocated deaths across component causes proportionately, estimated from all available data in the database. For selected causes (African trypanosomiasis, congenital syphilis, whooping cough, measles, typhoid and parathyroid, leishmaniasis, acute hepatitis E, and HIV/AIDS), we used natural history models based on information on incidence, prevalence, and case-fatality. We separately estimated cause fractions by aetiology for diarrhoea, lower respiratory infections, and meningitis, as well as disaggregations by subcause for chronic kidney disease, maternal disorders, cirrhosis, and liver cancer. For deaths due to collective violence and natural disasters, we used mortality shock regressions. For every cause, we estimated 95% UIs that captured both parameter estimation uncertainty and uncertainty due to model specification where CODEm was used. We constrained cause-specific fractions within every age-sex group to sum to total mortality based on draws from the uncertainty distributions. FINDINGS: In 2010, there were 52·8 million deaths globally. At the most aggregate level, communicable, maternal, neonatal, and nutritional causes were 24·9% of deaths worldwide in 2010, down from 15·9 million (34·1%) of 46·5 million in 1990. This decrease was largely due to decreases in mortality from diarrhoeal disease (from 2·5 to 1·4 million), lower respiratory infections (from 3·4 to 2·8 million), neonatal disorders (from 3·1 to 2·2 million), measles (from 0·63 to 0·13 million), and tetanus (from 0·27 to 0·06 million). Deaths from HIV/AIDS increased from 0·30 million in 1990 to 1·5 million in 2010, reaching a peak of 1·7 million in 2006. Malaria mortality also rose by an estimated 19·9% since 1990 to 1·17 million deaths in 2010. Tuberculosis killed 1·2 million people in 2010. Deaths from non-communicable diseases rose by just under 8 million between 1990 and 2010, accounting for two of every three deaths (34·5 million) worldwide by 2010. 8 million people died from cancer in 2010, 38% more than two decades ago; of these, 1·5 million (19%) were from trachea, bronchus, and lung cancer. Ischaemic heart disease and stroke collectively killed 12·9 million people in 2010, or one in four deaths worldwide, compared with one in five in 1990; 1·3 million deaths were due to diabetes, twice as many as in 1990. The fraction of global deaths due to injuries (5·1 million deaths) was marginally higher in 2010 (9·6%) compared with two decades earlier (8·8%). This was driven by a 46% rise in deaths worldwide due to road traffic accidents (1·3 million in 2010) and a rise in deaths from falls. Ischaemic heart disease, stroke, chronic obstructive pulmonary disease (COPD), lower respiratory infections, lung cancer, and HIV/AIDS were the leading causes of death in 2010. Ischaemic heart disease, lower respiratory infections, stroke, diarrhoeal disease, malaria, and HIV/AIDS were the leading causes of years of life lost due to premature mortality (YLLs) in 2010, similar to what was estimated for 1990, except for HIV/AIDS and preterm birth complications. YLLs from lower respiratory infections and diarrhoea decreased by 45-54% since 1990; ischaemic heart disease and stroke YLLs increased by 17-28%. Regional variations in leading causes of death were substantial. Communicable, maternal, neonatal, and nutritional causes still accounted for 76% of premature mortality in sub-Saharan Africa in 2010. Age standardised death rates from some key disorders rose (HIV/AIDS, Alzheimer's disease, diabetes mellitus, and chronic kidney disease in particular), but for most diseases, death rates fell in the past two decades; including major vascular diseases, COPD, most forms of cancer, liver cirrhosis, and maternal disorders. For other conditions, notably malaria, prostate cancer, and injuries, little change was noted. INTERPRETATION: Population growth, increased average age of the world's population, and largely decreasing age-specific, sex-specific, and cause-specific death rates combine to drive a broad shift from communicable, maternal, neonatal, and nutritional causes towards non-communicable diseases. Nevertheless, communicable, maternal, neonatal, and nutritional causes remain the dominant causes of YLLs in sub-Saharan Africa. Overlaid on this general pattern of the epidemiological transition, marked regional variation exists in many causes, such as interpersonal violence, suicide, liver cancer, diabetes, cirrhosis, Chagas disease, African trypanosomiasis, melanoma, and others. Regional heterogeneity highlights the importance of sound epidemiological assessments of the causes of death on a regular basis. FUNDING: Bill & Melinda Gates Foundation.

Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010.

BACKGROUND: Non-fatal health outcomes from diseases and injuries are a crucial consideration in the promotion and monitoring of individual and population health. The Global Burden of Disease (GBD) studies done in 1990 and 2000 have been the only studies to quantify non-fatal health outcomes across an exhaustive set of disorders at the global and regional level. Neither effort quantified uncertainty in prevalence or years lived with disability (YLDs). METHODS: Of the 291 diseases and injuries in the GBD cause list, 289 cause disability. For 1160 sequelae of the 289 diseases and injuries, we undertook a systematic analysis of prevalence, incidence, remission, duration, and excess mortality. Sources included published studies, case notification, population-based cancer registries, other disease registries, antenatal clinic serosurveillance, hospital discharge data, ambulatory care data, household surveys, other surveys, and cohort studies. For most sequelae, we used a Bayesian meta-regression method, DisMod-MR, designed to address key limitations in descriptive epidemiological data, including missing data, inconsistency, and large methodological variation between data sources. For some disorders, we used natural history models, geospatial models, back-calculation models (models calculating incidence from population mortality rates and case fatality), or registration completeness models (models adjusting for incomplete registration with health-system access and other covariates). Disability weights for 220 unique health states were used to capture the severity of health loss. YLDs by cause at age, sex, country, and year levels were adjusted for comorbidity with simulation methods. We included uncertainty estimates at all stages of the analysis. FINDINGS: Global prevalence for all ages combined in 2010 across the 1160 sequelae ranged from fewer than one case per 1 million people to 350,000 cases per 1 million people. Prevalence and severity of health loss were weakly correlated (correlation coefficient -0·37). In 2010, there were 777 million YLDs from all causes, up from 583 million in 1990. The main contributors to global YLDs were mental and behavioural disorders, musculoskeletal disorders, and diabetes or endocrine diseases. The leading specific causes of YLDs were much the same in 2010 as they were in 1990: low back pain, major depressive disorder, iron-deficiency anaemia, neck pain, chronic obstructive pulmonary disease, anxiety disorders, migraine, diabetes, and falls. Age-specific prevalence of YLDs increased with age in all regions and has decreased slightly from 1990 to 2010. Regional patterns of the leading causes of YLDs were more similar compared with years of life lost due to premature mortality. Neglected tropical diseases, HIV/AIDS, tuberculosis, malaria, and anaemia were important causes of YLDs in sub-Saharan Africa. INTERPRETATION: Rates of YLDs per 100,000 people have remained largely constant over time but rise steadily with age. Population growth and ageing have increased YLD numbers and crude rates over the past two decades. Prevalences of the most common causes of YLDs, such as mental and behavioural disorders and musculoskeletal disorders, have not decreased. Health systems will need to address the needs of the rising numbers of individuals with a range of disorders that largely cause disability but not mortality. Quantification of the burden of non-fatal health outcomes will be crucial to understand how well health systems are responding to these challenges. Effective and affordable strategies to deal with this rising burden are an urgent priority for health systems in most parts of the world. FUNDING: Bill & Melinda Gates Foundation.

Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010.

BACKGROUND: Measuring disease and injury burden in populations requires a composite metric that captures both premature mortality and the prevalence and severity of ill-health. The 1990 Global Burden of Disease study proposed disability-adjusted life years (DALYs) to measure disease burden. No comprehensive update of disease burden worldwide incorporating a systematic reassessment of disease and injury-specific epidemiology has been done since the 1990 study. We aimed to calculate disease burden worldwide and for 21 regions for 1990, 2005, and 2010 with methods to enable meaningful comparisons over time. METHODS: We calculated DALYs as the sum of years of life lost (YLLs) and years lived with disability (YLDs). DALYs were calculated for 291 causes, 20 age groups, both sexes, and for 187 countries, and aggregated to regional and global estimates of disease burden for three points in time with strictly comparable definitions and methods. YLLs were calculated from age-sex-country-time-specific estimates of mortality by cause, with death by standardised lost life expectancy at each age. YLDs were calculated as prevalence of 1160 disabling sequelae, by age, sex, and cause, and weighted by new disability weights for each health state. Neither YLLs nor YLDs were age-weighted or discounted. Uncertainty around cause-specific DALYs was calculated incorporating uncertainty in levels of all-cause mortality, cause-specific mortality, prevalence, and disability weights. FINDINGS: Global DALYs remained stable from 1990 (2·503 billion) to 2010 (2·490 billion). Crude DALYs per 1000 decreased by 23% (472 per 1000 to 361 per 1000). An important shift has occurred in DALY composition with the contribution of deaths and disability among children (younger than 5 years of age) declining from 41% of global DALYs in 1990 to 25% in 2010. YLLs typically account for about half of disease burden in more developed regions (high-income Asia Pacific, western Europe, high-income North America, and Australasia), rising to over 80% of DALYs in sub-Saharan Africa. In 1990, 47% of DALYs worldwide were from communicable, maternal, neonatal, and nutritional disorders, 43% from non-communicable diseases, and 10% from injuries. By 2010, this had shifted to 35%, 54%, and 11%, respectively. Ischaemic heart disease was the leading cause of DALYs worldwide in 2010 (up from fourth rank in 1990, increasing by 29%), followed by lower respiratory infections (top rank in 1990; 44% decline in DALYs), stroke (fifth in 1990; 19% increase), diarrhoeal diseases (second in 1990; 51% decrease), and HIV/AIDS (33rd in 1990; 351% increase). Major depressive disorder increased from 15th to 11th rank (37% increase) and road injury from 12th to 10th rank (34% increase). Substantial heterogeneity exists in rankings of leading causes of disease burden among regions. INTERPRETATION: Global disease burden has continued to shift away from communicable to non-communicable diseases and from premature death to years lived with disability. In sub-Saharan Africa, however, many communicable, maternal, neonatal, and nutritional disorders remain the dominant causes of disease burden. The rising burden from mental and behavioural disorders, musculoskeletal disorders, and diabetes will impose new challenges on health systems. Regional heterogeneity highlights the importance of understanding local burden of disease and setting goals and targets for the post-2015 agenda taking such patterns into account. Because of improved definitions, methods, and data, these results for 1990 and 2010 supersede all previously published Global Burden of Disease results. FUNDING: Bill & Melinda Gates Foundation.

A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010.

BACKGROUND: Quantification of the disease burden caused by different risks informs prevention by providing an account of health loss different to that provided by a disease-by-disease analysis. No complete revision of global disease burden caused by risk factors has been done since a comparative risk assessment in 2000, and no previous analysis has assessed changes in burden attributable to risk factors over time. METHODS: We estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent effects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010. We estimated exposure distributions for each year, region, sex, and age group, and relative risks per unit of exposure by systematically reviewing and synthesising published and unpublished data. We used these estimates, together with estimates of cause-specific deaths and DALYs from the Global Burden of Disease Study 2010, to calculate the burden attributable to each risk factor exposure compared with the theoretical-minimum-risk exposure. We incorporated uncertainty in disease burden, relative risks, and exposures into our estimates of attributable burden. FINDINGS: In 2010, the three leading risk factors for global disease burden were high blood pressure (7·0% [95% uncertainty interval 6·2-7·7] of global DALYs), tobacco smoking including second-hand smoke (6·3% [5·5-7·0]), and alcohol use (5·5% [5·0-5·9]). In 1990, the leading risks were childhood underweight (7·9% [6·8-9·4]), household air pollution from solid fuels (HAP; 7·0% [5·6-8·3]), and tobacco smoking including second-hand smoke (6·1% [5·4-6·8]). Dietary risk factors and physical inactivity collectively accounted for 10·0% (95% UI 9·2-10·8) of global DALYs in 2010, with the most prominent dietary risks being diets low in fruits and those high in sodium. Several risks that primarily affect childhood communicable diseases, including unimproved water and sanitation and childhood micronutrient deficiencies, fell in rank between 1990 and 2010, with unimproved water and sanitation accounting for 0·9% (0·4-1·6) of global DALYs in 2010. However, in most of sub-Saharan Africa childhood underweight, HAP, and non-exclusive and discontinued breastfeeding were the leading risks in 2010, while HAP was the leading risk in south Asia. The leading risk factor in Eastern Europe, most of Latin America, and southern sub-Saharan Africa in 2010 was alcohol use; in most of Asia, North Africa and Middle East, and central Europe it was high blood pressure. Despite declines, tobacco smoking including second-hand smoke remained the leading risk in high-income north America and western Europe. High body-mass index has increased globally and it is the leading risk in Australasia and southern Latin America, and also ranks high in other high-income regions, North Africa and Middle East, and Oceania. INTERPRETATION: Worldwide, the contribution of different risk factors to disease burden has changed substantially, with a shift away from risks for communicable diseases in children towards those for non-communicable diseases in adults. These changes are related to the ageing population, decreased mortality among children younger than 5 years, changes in cause-of-death composition, and changes in risk factor exposures. New evidence has led to changes in the magnitude of key risks including unimproved water and sanitation, vitamin A and zinc deficiencies, and ambient particulate matter pollution. The extent to which the epidemiological shift has occurred and what the leading risks currently are varies greatly across regions. In much of sub-Saharan Africa, the leading risks are still those associated with poverty and those that affect children. FUNDING: Bill & Melinda Gates Foundation.

Long-term exposure to outdoor air pollution and incidence of cardiovascular diseases.

BACKGROUND: Evidence based largely on US cohorts suggests that long-term exposure to fine particulate matter is associated with cardiovascular mortality. There is less evidence for other pollutants and for cardiovascular morbidity. By using a cohort of 836,557 patients age 40 to 89 years registered with 205 English general practices in 2003, we investigated relationships between ambient outdoor air pollution and incident myocardial infarction, stroke, arrhythmia, and heart failure over a 5-year period. METHODS: Events were identified from primary care records, hospital admissions, and death certificates. Annual average concentrations in 2002 for particulate matter with a median aerodynamic diameter <10 (PM10) and <2.5 microns, nitrogen dioxide (NO2), ozone, and sulfur dioxide at a 1 × 1 km resolution were derived from emission-based models and linked to residential postcode. Analyses were performed using Cox proportional hazards models adjusting for relevant confounders, including social and economic deprivation and smoking. RESULTS: While evidence was weak for relationships with myocardial infarction, stroke, or arrhythmia, we found consistent associations between pollutant concentrations and incident cases of heart failure. An interquartile range change in PM10 and in NO2 (3.0 and 10.7 µg/m, respectively) both produced a hazard ratio of 1.06 (95% confidence interval = 1.01-1.11) after adjustment for confounders. There was some evidence that these effects were greater in more affluent areas. CONCLUSIONS: This study of an English national cohort found evidence linking long-term exposure to particulate matter and NO2 with the development of heart failure. We did not, however, replicate associations for other cardiovascular outcomes that have been reported elsewhere.

Does outdoor air pollution induce new cases of asthma? Biological plausibility and evidence; a review.

It is widely accepted that air pollution can exacerbate asthma in those who already have the condition. What is less clear is whether air pollution can contribute to the initiation of new cases of asthma. Mechanistic evidence from toxicological studies, together with recent information on genes that predispose towards the development of asthma, suggests that this is biologically plausible, particularly in the light of the current understanding of asthma as a complex disease with a variety of phenotypes. The epidemiological evidence for associations between ambient levels of air pollutants and asthma prevalence at a whole community level is unconvincing; meta-analysis confirms a lack of association. In contrast, a meta-analysis of cohort studies found an association between asthma incidence and within-community variations in air pollution (largely traffic dominated). Similarly, a systematic review suggests an association of asthma prevalence with exposure to traffic, although only in those living very close to heavily trafficked roads carrying a lot of trucks. Based on this evidence, the U.K.'s Committee on the Medical Effects of Air Pollutants recently concluded that, overall, the evidence is consistent with the possibility that outdoor air pollution might play a role in causing asthma in susceptible individuals living very close to busy roads carrying a lot of truck traffic. Nonetheless, the effect on public health is unlikely to be large: air pollutants are likely to make only a small contribution, compared with other factors, in the development of asthma, and in only a small proportion of the population.

Impact of the 1990 Hong Kong legislation for restriction on sulfur content in fuel.

INTRODUCTION: After the implementation of a regulation restricting sulfur to 0.5% by weight in fuel on July 1, 1990, in Hong Kong, sulfur dioxide (SO2*) levels fell by 45% on average and as much as 80% in the most polluted districts (Hedley et al. 2002). In addition, a reduction of respiratory symptoms and an improvement in bronchial hyperresponsiveness in children were observed (Peters et al. 1996; Wong et al. 1998). A recent time-series study (Hedley et al. 2002) found an immediate reduction in mortality during the cool season at six months after the intervention, followed by an increase in cool-season mortality in the second and third years, suggesting that the reduction in pollution was associated with a delay in mortality. Proportional changes in mortality trends between the 5-year periods before and after the intervention were measured as relative risks and used to assess gains in life expectancy using the life table method (Hedley et al. 2002). To further explore the relation between changes in pollution-related mortality before and after the intervention, our study had three objectives: (1) to evaluate the short-term effects on mortality of changes in the pollutant mix after the Hong Kong sulfur intervention, particularly with changes in the particulate matter (PM) chemical species; (2) to improve the methodology for assessment of the health impact in terms of changes in life expectancy using linear regression models; and (3) to develop an approach for analyzing changes in life expectancy from Poisson regression models. A fourth overarching objective was to determine the relation between short- and long-term benefits due to an improvement in air quality. METHODS: For an assessment of the short-term effects on mortality due to changes in the pollutant mix, we developed Poisson regression Core Models with natural spline smoothers to control for long-term and seasonal confounding variations in the mortality counts and with covariates to adjust for temperature (T) and relative humidity (RH). We assessed the adequacy of the Core Models by evaluating the results against the Akaike Information Criterion, which stipulates that, at a minimum, partial autocorrelation plots should be between -0.1 and 0.1, and by examining the residual plots to make sure they were free from patterns. We assessed the effects for gaseous pollutants (NO2, SO2, and O3), PM with an aerodynamic diameter < or = 10 microm (PM10), and its chemical species (aluminum [Al], iron [Fe], manganese [Mn], nickel [Ni], vanadium [V], lead [Pb], and zinc [Zn]) using the Core Models, which were developed for the periods 5 years (or 2 years in the case of the sensitivity analysis) before and 5 years after the intervention, as well as in the10-year (or 7-year in the case of the sensitivity analysis) period pre- and post-intervention. We also included an indicator to separate the pre- and post-intervention periods, as well as the product of the indicator with an air pollution concentration variable. The health outcomes were mortality for all natural causes and for cardiovascular and respiratory causes, at all ages and in the 65 years or older age group. To assess the short- and long-term effects, we developed two methods: one using linear regression models reflecting the age-standardized mortality rate D(j) at day j, divided by a reference D(ref); and the other using Poisson regression models with daily mortality counts as the outcome variables. We also used both models to evaluate the relation between outcome variables and daily air pollution concentrations in the current day up to all previous days in the past 3 to 4 years. In the linear regression approach, we adjusted the data for temperature and relative humidity. We then removed season as a potential confounder, or deseasonalized them, by calculating a standard seasonal mortality rate profile, normalized to an annual average of unity, and dividing the mortality rates by this profile. Finally, to correct for long-term trends, we calculated a reference mortality rate D(ref)(j) as a moving average of the corrected and deseasonalized D(j) over the observation window. Then we regressed the outcome variable D(j)/D(ref) on an entire exposure sequence {c(i)} with lags up to 4 years in order to obtain impact coefficient f(i) from the regression model shown below: deltaD(j)/D (ref) = i(max)sigma f(i) c(j - i)(i = 0). The change in life expectancy (LE) for a change of units (deltac) in the concentration of pollutants on T(day)--representing the short interval (i.e., a day)--was calculated from the following equation (deltaL(pop) = average loss in life expectancy of an entire population): deltaL(pop) = -deltac T(day) infinity sigma (j = 0) infinity sigma f(i) (i = 0). In the Poisson regression approach, we fitted a distributed-lag model for exposure to previous days of up to 4 years in order to obtain the cumulative lag effect sigma beta(i). We fit the linear regression model of log(LE*/LE) = gamma(SMR - 1) + alpha to estimate the parameter gamma by gamma, where LE* and LE are life expectancy for an exposed and an unexposed population, respectively, and SMR represents the standardized mortality ratio. The life expectancy change per Ac increase in concentration is LE {exp[gamma delta c(sigma beta(i))]-1}. RESULTS: In our assessment of the changes in pollutant levels, the mean levels of SO2, Ni, and V showed a statistically significant decline, particularly in industrial areas. Ni and V showed the greatest impact on mortality, especially for respiratory diseases in the 5-year pre-intervention period for both the all-ages and 65+ groups among all chemical species. There were decreases in excess risks associated with Ni and V after the intervention, but they were nonsignificant. Using the linear regression approach, with a window of 1095 days (3 years), the losses in life expectancy with a 10-microg/m3 increase in concentrations, using two methods of estimation (one with adjustment for temperature and RH before the regression against pollutants, the other with adjustment for temperature and RH within the regression against pollutants), were 19.2 days (95% CI, 12.5 to 25.9) and 31.4 days (95% CI, 25.6 to 37.2) for PM10; and 19.7 days (95% CI, 15.2 to 24.2) and 12.8 days (95% CI, 8.9 to 16.8) for SO2. The losses in life expectancy in the current study were smaller than the ones implied by Elliott and colleagues (2007) and Pope and colleagues (2002) as expected since the observation window in our study was only 3 years whereas these other studies had windows of 16 years. In particular, the coefficients used by Elliott and colleagues (2007) for windows of 12 and 16 years were non-zero, which suggests that our window of at most 3 years cannot capture the full life expectancy loss and the effects were most likely underestimated. Using the Poisson regression approach, with a window of 1461 days (4 years), we found that a 10-microg/m3 increase in concentration of PM10 was associated with a change in life expectancy of -69 days (95% CI, -140 to 1) and a change of -133 days (95% CI, -172 to -94) for the same increase in SO2. The effect estimates varied as expected according to most variations in the sensitivity analysis model, specifically in terms of the Core Model definition, exposure windows, constraint of the lag effect pattern, and adjustment for smoking prevalence or socioeconomic status. CONCLUSIONS: Our results on the excess risks of mortality showed exposure to chemical species to be a health hazard. However, the statistical power was not sufficient to detect the differences between the pre- and post-intervention periods in Hong Kong due to the data limitations (specifically, the chemical species data were available only once every 6 days, and data were not available from some monitoring stations). Further work is needed to develop methods for maximizing the information from the data in order to assess any changes in effects due to the intervention. With complete daily air pollution and mortality data over a long period, time-series analysis methods can be applied to assess the short- and long-term effects of air pollution, in terms of changes in life expectancy. Further work is warranted to assess the duration and pattern of the health effects from an air pollution pulse (i.e., an episode of a rapid rise in air pollution) so as to determine an appropriate length and constraint on the distributed-lag assessment model.

Outdoor air pollution and respiratory health in patients with COPD.

OBJECTIVES: Time series studies have shown adverse effects of outdoor air pollution on mortality and hospital admissions in patients with chronic obstructive pulmonary disease (COPD) but panel studies have been inconsistent. This study investigates short-term effects of outdoor nitrogen dioxide, ozone, sulfur dioxide, particulate matter (PM(10)) and black smoke on exacerbations, respiratory symptoms and lung function in 94 patients with COPD in east London. METHODS: Patients were recruited from an outpatient clinic and were asked to complete daily diary cards (median follow-up 518 days) recording exacerbations, symptoms and lung function, and the amount of time spent outdoors. Outdoor air pollution exposure (lag 1 day) was obtained from local background monitoring stations. RESULTS: Symptoms but not lung function showed associations with raised pollution levels. Dyspnoea was significantly associated with PM(10) (increase in odds for an IQR change in pollutant: 13% (95% CI 4% to 23%)) and this association remained after adjustment for other the pollutants measured. An IQR increase in nitrogen dioxide was associated with a 6% (0-13%) increase in the odds of a symptomatic fall in peak flow rate. The corresponding effect sizes for PM(10) and black smoke were 12% (2-25%) and 7% (1-13%), respectively. CONCLUSION: It is concluded that outdoor air pollution is associated with important adverse effects on symptoms in patients with COPD living in London.

The impact of the congestion charging scheme on air quality in London. Part 1. Emissions modeling and analysis of air pollution measurements.

On February 17, 2003, a congestion charging scheme (CCS*) was introduced in central London along with a program of traffic management measures. The scheme operated Monday through Friday, 7 AM to 6 PM. This program resulted in an 18% reduction in traffic volume and a 30% reduction in traffic congestion in the first year (2003). We developed methods to evaluate the possible effects of the scheme on air quality: We used a temporal-spatial design in which modeled and measured air quality data from roadside and background monitoring stations were used to compare time periods before (2001-2002) and after (2003-2004) the CCS was introduced and to compare the spatial area of the congestion charging zone (CCZ) with the rest of London. In the first part of this project, we modeled changes in concentrations of oxides of nitrogen (NOx), nitrogen dioxide (NO2), and PM10 (particles with a mass median aerodynamic diameter < or = 10 microm) across the CCZ and in Greater London under different traffic and emission scenarios for the periods before and after CCS introduction. Comparing model results within and outside the zone suggested that introducing the CCS would be associated with a net 0.8-microg/m3 decrease in the mean concentration of PM10 and a net 1.7-ppb decrease in the mean concentration of NOx within the CCZ. In contrast, a net 0.3-ppb increase in the mean concentration of NO2 was predicted within the zone; this was partly explained by an expected increase in primary NO2 emissions due to the introduction of particle traps on diesel buses (one part of the improvements in public transport associated with the CCS). In the second part of the project, we established a CCS Study Database from measurements obtained from the London Air Quality Network (LAQN) for air pollution monitors sited to measure roadside and urban background concentrations. Fully ratified (validated) 15-minute mean carbon monoxide (CO), nitric oxide (NO), NO2, NOx, PM10, and PM2.5 data from each chosen monitoring site for the period from February 17, 2001, to February 16, 2005, were transferred from the LAQN database. In the third part of our project, these data were used to compare geometric means for the 2 years before and the 2 years after the CCS was introduced. Temporal changes within the CCZ were compared with changes, over the same period, at similarly sited (roadside or background) monitors in a control area 8 km distant from the center of the CCZ. The analysis was confined to measurements obtained during the hours and days on which the scheme was in operation and focused on pollutants derived from vehicles (NO, NO2, NOx, PM10, and CO). This set of analyses was based on the limited data available from within the CCZ. When compared with data from outside the zone, we did not find evidence of temporal changes in roadside measurements of NOx, NO, and NO2, nor in urban background concentrations of NOx. (The latter result, however, concealed divergent trends in NO, which fell, and NO2, which rose.) Although based upon fewer stations, there was evidence that background concentrations of PM10 and CO fell within the CCZ compared with outside the zone. We also analyzed the trends in background concentrations for all London monitoring stations; as distance from the center of the CCZ increased, we found some evidence of an increasing gradation in NO and PM10 concentrations before versus after the intervention. This suggests a possible intermediate effect on air quality in the area immediately surrounding the CCZ. Although London is relatively well served with air quality monitoring stations, our study was restricted by the availability of only a few monitoring sites within the CCZ, and only one of those was at a roadside location. The results derived from this single roadside site are not likely to be an adequate basis for evaluating this complex urban traffic management scheme. Our primary approach to assessing the impact of the CCS was to analyze the changes in geometric mean pollutant concentrations in the 2 years before and 2 years after the CCS was introduced and to compare changes at monitoring stations within the CCZ with those in a distant control area (8 km from the CCZ center) unlikely to be influenced by the CCS. We saw this as the most robust analytical approach with which to examine the CCS Study Database, but in the fourth part of the project we did consider three other approaches: ethane as an indicator of pollution dispersion; the cumulative sum (CUSUM) statistical technique; and bivariate polar plots for local emissions. All three were subsequently judged as requiring further development outside of the scope of this study. However, despite their investigative nature, each technique provided useful information supporting the main analyses. The first method used ethane as a dispersion indicator to remove the inherent variability in air pollutant concentrations caused by changes in meteorology and atmospheric dispersion. The technique had the potential to ascertain more accurately the likely impacts of the CCS on London's air quality. Although this novel method appeared promising over short time periods, a number of concerns arose about whether the spatial and temporal variability of ethane over longer time periods would be representative of meteorologic conditions alone. The major strength of CUSUM, the second method, is that it can be used to identify the approximate timing of changes that may have been caused by the CCS. This ability is weakened, however, by the effects of serial correlation (the correlation of data among measurements in successive time intervals) within air pollution data that is caused by seasonality and long-term meteorologic trends. The secure interpretation of CUSUM requires that the technique be adapted to take proper account of the underlying correlation between measurements without the use of smoothing functions that would obscure a stepped change in concentrations. Although CUSUM was not able to provide a quantitative estimation of changes in pollution levels arising from the introduction of the CCS, the strong signals that were identified were considered in the context of other results from the study. The third method, bivariate polar plots, proved useful. The plots revealed important characteristics of the data from the only roadside monitoring site within the CCZ and highlighted the importance of considering prevailing weather conditions when positioning a roadside monitor. The technique would benefit from further development, however, in transforming the qualitative assessment of change into a quantitative assessment and including an estimate of uncertainty. Research is ongoing to develop this method in air-quality time-series studies. Overall, using a range of measurement and modeling approaches, we found evidence of small changes in air quality after introduction of the CCS. These include small decreases in PM10, NO, and CO. The possibility that some of these effects might reflect more general changes in London's air quality is suggested by the findings of somewhat similar changes in geometric means for weekends, when the CCS was not operating. However, since some evidence suggests that the CCS also had an impact on traffic volume on weekends, the CCS remains as one possible explanation for the observed pattern of changes in pollutant concentrations. In addition, the CCS was just one of a number of traffic and emission reduction schemes introduced in London over the 4-year study period; if the other measures had an impact in central London, they might partly explain our findings. Although not the aim of this study, it is important to consider how the trends we observed might be translated into health effects. For example, given that London already has NO2 concentrations in excess of the permitted limit value, we do not know what the effects of an increase in NO2 created by diesel-exhaust after-treatment for particles might mean for health. Further, although it is not likely that NO affects health, the decrease in NO concentrations is likely associated with an increase in ozone concentrations (a pollutant associated with health effects), as has been seen in recent years in London. These and other similar issues require further investigation. Although the CCS is a relatively simple traffic management scheme in the middle of a major urban environment, analyzing its possible impact on air quality was found to be far from straightforward. Using a range of modeling and monitoring approaches to address the impact of the scheme revealed that each technique has its own advantages and limitations. The placement of monitoring sites and the availably of traffic count data were also identified as key issues. The most compelling lesson we take away from this study is that such work is impossible to undertake without a coherent multi-disciplinary team of skilled researchers. In conclusion, our study suggests that the introduction of the CCS in 2003 was associated with small temporal changes in air pollutant concentrations in central London compared with outer areas. However, attributing the cause of these changes to the CCS alone is not appropriate because the scheme was introduced at a time when other traffic and emissions interventions, which might have had a more concentrated effect in central London, were also being implemented.

The impact of the congestion charging scheme on air quality in London. Part 2. Analysis of the oxidative potential of particulate matter.

There is growing scientific consensus that the ability of inhaled particulate matter (PM*) to elicit oxidative stress both at the air-lung interface and systemically might underpin many of the acute and chronic respiratory and cardiovascular responses observed in exposed populations. In the current study (which is part two of a two-part HEI study of a congestion charging scheme [CCS] introduced in London, United Kingdom, in 2003), we tested the hypothesis that the reduction in vehicle numbers and changes in traffic composition resulting from the introduction of the CCS would result in decreased concentrations of traffic-specific emissions, both from vehicle exhaust and other sources (brake wear and tire wear), and an associated reduction in the oxidative potential of PM with an aerodynamic diameter < or = 10 microm (PM10). To test this hypothesis, we obtained, extracted, and analyzed tapered element oscillating microbalance (TEOM) PM10 filters from six monitoring sites within, bordering, or outside the area of the congestion charging zone (CCZ) for the 3 years before and after the introduction of the scheme. In addition, from January 2005, TEOM PM10 filters were obtained from an additional 10 sites outside the zone in order to perform the first-ever assessment of within-city spatial variability in the oxidative potential of PM10. Although London's PM10 was found to have remarkably high oxidative potential, it varied markedly between the studied sites, with evidence of increased potential at roadside locations compared with urban background locations. This difference appeared to reflect increased concentrations of copper (Cu), barium (Ba), and bioavailable iron (Fe) in PM10 collected at the roadside sites. PM10's oxidative potential after the introduction of the CCS did not change at the one urban background site within the zone. Yet compositional changes in PM10 were noted at the same site, including significant decreases in Cu and zinc (Zn) content, probably reflecting brake and tire wear (compared with increases in these metals at all sites outside the zone in the 3 years since the scheme's introduction). This pattern of results is consistent with observations of increased vehicle use throughout London in recent years and decreases in the number of vehicles entering the zone since the scheme's introduction.